Synthetic MRI

Key takeaways

Synthetic MRI belongs to computer science; place it in that map before memorising details.
Learn the definition first, then one example that makes the definition concrete.
Connect Synthetic MRI to a quantity you can measure, compute or draw — that is where exam questions come from.
Reproduce the core statement of Synthetic MRI from memory before moving on to harder problems.

Reference excerpt

Synthetic MRI is a simulation method in Magnetic Resonance Imaging (MRI), for generating contrast weighted images based on measurement of tissue properties. The synthetic (simulated) images are generated after an MR study, from parametric maps of tissue properties. It is thereby possible to generate several contrast weightings from the same acquisition. This is different from conventional MRI, where the signal acquired from the tissue is used to generate an image directly, often generating only one contrast weighting per acquisition. The synthetic images are similar in appearance to those normally acquired with an MRI scanner. The parametric maps can be computed from a particular MRI acquisition designed to measure the tissue parameters, known as quantification. Using the maps, which contains the measured parameters for each voxel, virtual scanner settings that correspond to those used in conventional scan are given. These settings can be echo time (TE) and repetition time (TR) for a spin-echo (SE) sequence or TE, TR and inversion time (TI) for an inversion recovery (IR, FLAIR, STIR, PSIR, FSE-IR, TIRM) sequence. Using the signal equations for different types of MR acquisitions, it is then possible to calculate what a conventional image would look like. Calculating the images based on maps and scanner settings is called synthesizing the images.

History of Synthetic MRI Synthetic MRI was proposed as early as 1984 Bielke et al. and 1985 by Bobman et al. Although scientifically interesting, the method was cumbersome for clinical use. The acquisition duration was too long for a patient to lie still, and the computations needed for quantification were too demanding for the standard commercial computers of the day. For the computers, the problem was both overall computational speed and handling numbers over 16 bit. To perform the synthetization, Lee et al. suggested a purpose-built computer that would be able to synthesize images in about 600 ms. It would use a look-up table to save computations and be capable of handling number sizes up to 28 bit. This device did however not solve the calculation needs for quantification, nor the long acquisition times. MR Image Expert, a software to create synthetic magnetic resonance images, was introduced in the late 1980s. It was aimed at educational and research purposes, among them contrast agent applications. Since 1989, more than 12,000 licenses of this software have been distributed. In 2004 the first rapid acquisition and quantification method for creating parametric maps was invented. This new acquisition method performs 8 acquisitions at 4 different excitation delays, giving 8 values to estimate T1, T2, PD and M0 for each imaged voxel. There are also other methods for creating the parametric maps being researched. Most notable is Magnetic Resonance Fingerprinting. This method uses a randomized acquisition that creates a unique response depending on T1-T2-PD values of a voxel. This unique response is then matched against a database of response from all possible T1-T2-PD combinations.

Features of Synthetic MRI Creating a synthetic contrast weighted image is very quick and the scanner setting parameters can be changed interactively. This is different from acquiring the images directly on the MRI scanner with a pulse sequence where it might take several minutes to get new images with different settings. Synthetic MRI also enables the possibility to create new image contrast weightings after the scan has been completed and the patient has left the hospital. A prospective multi-reader, multi-case, multi-center clinical trial of overall image quality of synthetic MRI compared to conventional MRI in a general neuroimaging population has shown that synthetic MRI is non-inferior to conventional imaging.

The parametric maps Synthetic MRI is based on parametric maps. These maps are generated by using the MR scanner not to acquire an image, but to measure magnetic properties in the tissue. The parametric maps can for example be longitudinal (T1) and transversal (T2) relaxation times as well as either proton density (PD) or apparent equilibrium magnetization (M0). It is conceivable to use other parameters depending on the acquisition sequence to be simulated, but these are sufficient for spin-echo and inversion recovery acquisitions.

Governing equations for Synthetic MRI The synthetic images are calculated using the signal equation of the type of image synthesized. The signal equation is a formula for calculating the signal intensity, i.e. the numerical value, of an image pixel. The signal intensity S in a pixel depends on the tissue properties T1, T2 and PD of the corresponding voxel, as well as the echo time TE and repetition time TR.

The equation for synthesizing a fast spin-echo (FSE) image is:

S = P D ⋅ exp ⁡ ( − T E / T 2 ) ⋅ ( 1 − exp ⁡ ( − ( T R − E T L ⋅ E S P ) / T 1 ) ) {\displaystyle S=PD\cdot \exp(-TE/T_{2})\cdot (1-\exp(-(TR-ETL\cdot ESP)/T_{1}))}

Here ETL is the echo train length, and ESP is the spacing of echoes in the echo train. If the acquisition simulated has the echo in the middle of ETL used for central k-space then E T L ⋅ E S P = 2 ⋅ T E {\displaystyle ETL\cdot ESP=2\cdot TE} . The scanner parameters TE and TR are the same over the entire slice/volume that is being synthesized but the parameters T1, T2, PD and the resulting signal S are different for different voxels. The parameters T1, T2, and PD correspond to physical properties of the tissue within the voxel. For an inversion recovery (IR) sequence the equation is:

… excerpt ends here. Continue reading the full article.

Worked examples

Example 1 — a first encounter with Synthetic MRI

Start with the simplest possible case. Write down what Synthetic MRI claims or describes in one sentence, then invent the smallest concrete situation in which that sentence is true. In computer science, the smallest case is usually a single object, a single equation or a single measurement. Check that every symbol or term in your sentence has a meaning in that case.

Example 2 — changing one variable

Take the situation from Example 1 and change exactly one quantity: double it, halve it, or set it to zero. Predict what should happen to Synthetic MRI before you calculate. Comparing your prediction with the result is the fastest way to find out whether you understand the idea or only the words.

Example 3 — an exam-style question

Typical questions about Synthetic MRI ask you to (a) state it precisely, (b) apply it to given data, and (c) explain a limitation. Practise writing all three answers in under five minutes; the third part is what separates a full-mark answer from an average one.

Applications of Synthetic MRI

In research: Synthetic MRI appears in computer science research whenever the underlying quantities have to be modelled precisely. Papers usually cite it as a starting assumption and then explore where it breaks down.
In technology and industry: Engineering practice reuses Synthetic MRI in design rules, simulations and safety margins. Knowing the idea lets you read a specification sheet and understand why the numbers look the way they do.
In the classroom: Synthetic MRI is common in secondary-school and first-year university syllabi. It links to neighbouring topics Computing in medical imaging, Magnetic resonance imaging, so understanding it makes those chapters shorter.
In everyday life: Look for Synthetic MRI outside the textbook — in sport, cooking, traffic, electronics or the sky above you. An example you found yourself is remembered far longer than one you were given.

How to study Synthetic MRI in 20 minutes

Read the reference excerpt below once, without taking notes.
Close the page and write down what Synthetic MRI means in your own words.
Compare your version with the excerpt and mark what you missed.
Work through the three examples above with pen and paper.
Explain Synthetic MRI out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is Synthetic MRI in simple terms?

Synthetic MRI is a simulation method in Magnetic Resonance Imaging (MRI), for generating contrast weighted images based on measurement of tissue properties. The synthetic (simulated) images are generated after an MR study, from parametric maps of tissue properties.

Why does Synthetic MRI matter?

Because it connects several computer science ideas at once: it gives you a definition you can apply, a quantity you can calculate, and a way to check whether a result is plausible.

How should I study Synthetic MRI?

Read the excerpt, restate it from memory, then work through the examples and applications listed on this page. The five-step study plan above takes about twenty minutes.

What does this page cover?

It gives you a compact reference excerpt plus original lgStudy explanations, examples, applications and study material on Synthetic MRI.